Software and AI rarely fail at the idea. They fail in the last mile.

A forward-deployed engineer is the role the AI labs and Palantir use for an engineer who goes to the customer, works inside their company and builds until the product delivers. The good ones are rare, expensive and in demand, and most companies will not find one in a six-month search. We offer the same way of working as an engagement: one industry expert who, with coding agents doing the typing, delivers what a team did before, at a fixed price per outcome instead of a salary, with everything written into your repositories so nothing leaves in one head, and with a team built around him when an outcome needs more hands. You get the outcome ownership of the role without the hiring, the payroll or the search.

Not the full two weeks. We spend a few days scoping what you hand us: the integrations it touches, the people who will use it, what done looks like in production. Then you get a fixed price and a timeline. If scoping turns up something more valuable than the original ask, we tell you and let you decide.

That is the normal starting point, and it is what discovery is for. We sit with the people who do the work and find the steps that are slow or expensive. Some of those are AI shaped: reading documents, classifying requests, drafting answers. Many are not, and need ordinary software or an integration. We rank by return, not by technology, and we say when a model does not earn its place.

Fixed price per outcome, in every tier. For an outcome you hand us, a few days of scoping set the price; for work we find, the two-week discovery does. Both are short and priced separately, so you never pay for a build you have not scoped. In the Advanced tier the outcomes are planned a quarter at a time, each with its own price.

You do, from the first commit. Everything lives in your repositories, your pipelines and your cloud account, including the prompts, evaluation sets and configuration behind any AI feature. Your data never trains anyone's model. If we part ways tomorrow, you lose nothing but us.

Your developers get a working session, not just documentation, and the decisions we made are written down with the lint rules that enforce them. After that you choose: run it yourself, keep us on call, or take the next outcome from the list together. There is no maintenance contract you did not plan for, and the workshops exist for teams that want to keep the pace on their own.

We involve them in discovery, not at sign-off. Your guidelines, review process and release gates apply to us as they apply to your own developers, and that includes how any AI feature handles data. We have worked under the requirements of utilities, healthcare, defence and government. If you have no such requirements yet, we set up the sensible defaults so you are not the company that adds security after the breach.

That is the most common case. We build inside your workspace, respect its module boundaries and, where they are missing, help put them in. If the codebase itself is the problem, the architecture assessment is the place to start: a fixed-scope outside read that becomes the first part of discovery.

Roberto Heckers, the person you talk to first. With coding agents handling the typing, one senior engineer who knows the whole stack covers what used to need several people, and that keeps communication direct and the decisions consistent. When an outcome genuinely needs more hands, engineers we have worked with before join under the same lead and the same rules.

No. That is the moment where the most money is saved or wasted. Discovery cuts the idea down to a first version real users can try, and we set up the repositories, pipeline and cloud account in your name from day one, so whoever you hire later walks into a working project instead of a supplier's black box.